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Supply chain revamping (SCR) is an important strategy for firms to improve their supply chain operations in a rapidly changing environment. The purpose of this study is to shed light on the impact of SCR on shareholder value.

Based on Signaling Theory and 184 SCR announcements published by US-listed firms from 2013 to 2018, this study employs event study methodology and empirically examines three issues: Antecedents of SCRs; Primary purposes and actions of SCRs; In addition to the impact of SCRs on shareholder value using stock returns, we also examined the factors that can influence the extent of stock returns.

Firstly, our results indicate that SCRs are primarily driven by firms’ poor prior performance, CEO turnover and external control threats (ECTs). Secondly, the stock market favors SCRs aiming to meet customer needs and those accomplished through network remodel. However, the market reacts negatively to SCRs aiming at cutting costs, improving poor performance, and those implemented through network trim. Finally, the cross-sectional analysis indicates that shareholders prefer firms operating in more competitive or faster-growing industries and those adopting an expansionist strategy than those adopting a streamlining strategy.

Our study provides managers with valuable insights into when firms can benefit from initiating SCRs not only by examining the purposes and actions of SCRs but also by examining the industry- and strategy-specific moderators. Our study illuminates the conditions under which SCR will positively affect shareholder value. Additionally, this study contributes to the existing literature by deepening the understanding of the impact of supply chain decisions on firm performance and identifying the marginal conditions under which the stock market will react positively to SCR announcements.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.

The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.

The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.

The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.

This study aims to investigate the situational factors that intensify the impact of leader bottom-line mentality (BLM) on employee pro-self-unethical behavior. In particular, the moderating role of contingent rewards and punishments is evaluated under the lens of situational strength theory.

Data were collected from 218 full-time employees working in the USA in a time-lagged study and analyzed using SPSS Process Macro.

Statistical analysis reveal contingent rewards and punishments significantly moderate the positive relationship between BLM and pro-self-unethical behavior.

This paper highlights the need for more balanced reward systems that incorporate moral conduct into work performance. It also emphasizes the role of robust accountability and monitoring systems in minimizing employees’ unethical behavior.

To the best of the authors’ knowledge, this is the first study to investigate the moderating role of contingent rewards and punishments on the relationship between leader BLM and subordinate pro-self-unethical behavior. Moreover, it provides significant empirical support to situational strength theory.

This study investigates the influence of nonfinancial 8-K disclosures released during the earnings announcement window on the abnormal trading activities of individual investors.

We employ regression analysis in this empirical study to examine the impact of nonfinancial 8-K filings on individual investors' abnormal trading activities.

Our results reveal that individual investors exhibit higher levels of abnormal trading activities when firms release nonfinancial 8-Ks during the (0,1) window of earnings announcements. This effect is observed for both buyer-initiated and seller-initiated transactions and is particularly pronounced for firms reporting an operating loss. Negative sentiment in 8-Ks significantly intensifies such effect. Additionally, we find that buy-sell consensus increases significantly with concurrent nonfinancial 8-Ks. This suggests that 8-Ks may reduce information noise, leading individuals to trade with greater conviction.

Our study examines the joint influence of nonfinancial 8-Ks and earnings announcements on individual investors' trading activities, thereby providing a novel perspective on the mechanisms through which 8-K filings affect individual investors' trading behaviors.

The single-supplier multi-retailer cold chain is a widely adopted type of supply chain in the real-world food industry. This paper aims to consider the problem of effectively designing and managing a single-supplier multi-retailer cold chain for fresh produce with deterministic demand to minimize the total cost, which includes cooling, loss of value and carbon emission costs.

The global stability index (GSI) method and the non-Arrhenius model are integrated to describe the behavior of food quality degradation. The power-of-two (PoT) policy is adopted in determining the coordinated replenishment policies for the suppliers and retailers, and an appropriate wholesale price structure that can achieve the coordination of the chain is presented.

The properties of the cold chain are uncovered, and an appropriate wholesale price scheme that achieves chain coordination with the optimal PoT decision is provided. In the numerical examples, different scenarios are investigated, and it is found that the cold chain parameters influence the optimal decisions in certain ways.

The PoT policy – an efficient policy to determine the replenishment strategy – has not been adopted in finding the solution of a single-supplier multi-retailer cold chain in the literature. Also, no study has compared the uncoordinated and coordinated cold chain. Moreover, in the existing literature, the wholesale price is usually a constant rather than having a coordinated scheme. This research aims to fill these research gaps.

This study investigates the media activities of firms issuing private equity placements and seasoned equity offerings in Taiwan, as firms have incentives to manage media coverage to influence their stock prices during private equity placement.

We collect a corpus of news stories and transform the news into term sets based on the part of speech. Then, we refer to Cecchini et al. (2010) to classify the news terms into positive, negative, and usual categories. Next, we employ the SVM algorithm to perform the classification tasks and the term frequency method to perform the text mining task. In last, we use a multiple regression model to verify the hypotheses.

We determine that issuing firms in a private placement have substantially more positive news stories and fewer negative news stories than those in public offerings. Furthermore, we evidence that the media management effects of postequity issues are more active than those of preequity issues. Finally, our results demonstrate that the timing and content of financial media coverage among different equity issuance methods may be biased by firm management. According to previous studies, they may attempt to manipulate stock prices to increase the number of highly profitable insider stakeholders.

To our knowledge, this is the first study to investigate that if private placement will associate with more active media management than the public offerings. According to our results of the difference-in-means test, the public offerings market may control news coverage; however, this result is inconsistent with that of the regression results. The private placements market may also exercise media management in the “before announcement day” and “after announcement day” periods by increasing positive news and reducing negative news.

This paper aims to explore the outcomes of an analysis on day by day task – journey planning conduct of senior citizens by using a modern dynamic model and a family unit travel overview, gathered in Bhubaneswar, Odisha, of India in 2018. The task-journey planning display assumes an unique time–space-constrained planning development.

The main commitment of this paper is to reveal day by day task – journey planning conduct through a comprehensive dynamic framework. Numerous behavioural subtleties are revealed by the subsequent empirical model. These incorporate the role that income plays in directing outside time consumption decisions of senior citizens. Senior citizens in the most elevated and least salary classes will in general have minor varieties in time consumption decisions than those in middle pay classifications. Generally speaking, the time consumption decisions become progressively steady with expanding age, demonstrating that more task durations and lower task recurrence become progressively predominant with increasing age.

Day by day task-type and area decisions reveal a reasonable irregular utility-amplifying level headed conduct of senior residents. Unmistakably expanding spatial availability to different task areas is an urgent factor in characterizing every day outside task interest of senior residents. It is likewise evident that the assorted variety of outside task-type decisions decreases with rise in age and senior citizens are major touchy to auto journey hour than to travel or non-mechanized journey hour.

The fundamental constraint to the dynamic structure is that the mode decision model was viewed as exogenic to the demonstrating framework. The essential purpose behind this supposition that was that senior citizens in the Bhubaneswar are overwhelmingly customers of the local car. Coordination of the mode decision display part inside this structure would deliver a full task-based journey request model that could catch trip age, starting times, outing circulation and mode decision using a solitary demonstrating framework.

This study examines the semi-strong market efficiency of the Indian agricultural commodity market in light of market reforms and policies. This study investigates whether the market reforms have boosted the speed of price adjustment and influenced the market quality.

The study used the daily data of nine agricultural commodities. To precisely capture the effects of market microstructure changes, this study split the whole data into pre- and post-ban and pre- and post-reform eras. To ascertain the velocity of price adjustment, the authors used the ARMA (1,1) model, and the ADD VRatio was employed to identify the price movement on a specific day.

This study found that full incorporation of information happens sometimes. The authors noticed no gradual progress in the quickness of price adjustment. Since both methods suggested the same result for the period, the authors confirm that market microstructure changes do not enhance market quality.

This research has implications for academicians, policymakers and market players.

The paper has twofold novelty. First, this is a contemporary topic, and very few studies have been done in the Indian agriculture context. Second, the study has implications for policymakers and government because it highlights the effects of structural changes on market quality and market efficiency.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.